Name
Title | ||
|---|---|---|
Predicting Protein-Protein Interactions based on Biological Information using Extreme Gradient Boosting |
Abstract | ||
|---|---|---|
Protein-protein interactions (PPIs)are vital to numerous biological processes. Computational methods have been used to predict PPIs from protein sequences. Several studies utilize popular algorithms such as Support Vector Machines (SVM)and Random Forest (RF)for detecting PPIs. The hypothesis of this study is that Extreme Gradient Boosting (XGBoost), which uses gradient boosted decision trees as the base classifier, can produce comparable results to those produced by SVM and RF. Based on the experimental results for the assembled protein interaction dataset, XGBoost produced better results than SVM and RF for the majority of the metrics used. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CIBCB.2019.8791241 | 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) |
Keywords | DocType | ISBN |
Protein-Protein Interaction,Machine Learning,Ensemble Learning,Extreme Gradient Boosting,Support Vector Machine,Random Forest | Conference | 978-1-7281-1463-7 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jerome Cary Beltran | 1 | 0 | 0.34 |
| Paolo Valdez | 2 | 0 | 0.34 |
| Prospero Naval | 3 | 2 | 0.69 |